| Abstract |
Contact tracing is an effective measure by which to prevent
further infections in public transportation systems. Considering the
large number of people infected during the COVID-19 pandemic, digital
contact tracing is expected to be quicker and more effective than
traditional manual contact tracing, which is slow and labor-intensive.
In this study, we introduce a knowledge graph-based framework for fusing
multi-source data from public transportation systems to construct
contact networks, design algorithms to model epidemic spread, and verify
the validity of an effective digital contact tracing method. In
particular, we take advantage of the trip chaining model to integrate
multi-source public transportation data to construct a knowledge graph. A
contact network is then extracted from the constructed knowledge graph,
and a breadth-first search algorithm is developed to efficiently trace
infected passengers in the contact network. The proposed framework and
algorithms are validated by a case study using smart card transaction
data from transit systems in Xiamen, China. We show that the knowledge
graph provides an efficient framework for contact tracing with the
reconstructed contact network, and the average positive tracing rate is
over 96%. |